Table 1 and S3
- The following models were used to create Table 1 and S3
Hill-Richness - 85% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 145.9 159.6 -63.9 127.9 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 1.681 1.296
## Residual 1.345 1.160
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.34
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 21.627418 8.951937 2.416 0.01569 *
## poly(HD_Cont, 2)1 2.803237 3.003803 0.933 0.35070
## poly(HD_Cont, 2)2 -7.392928 2.480773 -2.980 0.00288 **
## MHWAfter -3.386123 0.397763 -8.513 < 2e-16 ***
## NPP -0.010488 0.008529 -1.230 0.21880
## poly(HD_Cont, 2)1:MHWAfter -3.881003 2.319335 -1.673 0.09426 .
## poly(HD_Cont, 2)2:MHWAfter -5.319014 2.319335 -2.293 0.02183 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Shannon - 85% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 139.7 153.4 -60.8 121.7 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.6723 0.8199
## Residual 1.5317 1.2376
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.53
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 18.874476 6.999493 2.697 0.007006 **
## poly(HD_Cont, 2)1 1.486186 2.494737 0.596 0.551357
## poly(HD_Cont, 2)2 -7.202565 2.114405 -3.406 0.000658 ***
## MHWAfter -3.981801 0.424495 -9.380 < 2e-16 ***
## NPP -0.009140 0.006667 -1.371 0.170425
## poly(HD_Cont, 2)1:MHWAfter -1.295889 2.475211 -0.524 0.600594
## poly(HD_Cont, 2)2:MHWAfter -1.464260 2.475211 -0.592 0.554139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Simpson - 85% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_85 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 134.1 147.8 -58.0 116.1 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.2205 0.4696
## Residual 1.5720 1.2538
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.57
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 15.797135 5.856909 2.697 0.006993 **
## poly(HD_Cont, 2)1 0.431847 2.203590 0.196 0.844630
## poly(HD_Cont, 2)2 -6.894496 1.904976 -3.619 0.000296 ***
## MHWAfter -4.149528 0.430046 -9.649 < 2e-16 ***
## NPP -0.007107 0.005578 -1.274 0.202606
## poly(HD_Cont, 2)1:MHWAfter 0.006452 2.507578 0.003 0.997947
## poly(HD_Cont, 2)2:MHWAfter 0.650767 2.507578 0.260 0.795234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Richness - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 160.6 174.3 -71.3 142.6 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 2.248 1.499
## Residual 2.236 1.495
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.24
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 26.33526 10.70615 2.460 0.01390 *
## poly(HD_Cont, 2)1 2.25169 3.63551 0.619 0.53568
## poly(HD_Cont, 2)2 -7.56427 3.01898 -2.506 0.01223 *
## MHWAfter -3.40016 0.51287 -6.630 3.36e-11 ***
## NPP -0.01337 0.01020 -1.310 0.19006
## poly(HD_Cont, 2)1:MHWAfter -3.38301 2.99051 -1.131 0.25795
## poly(HD_Cont, 2)2:MHWAfter -9.27442 2.99051 -3.101 0.00193 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Shannon - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 142.2 155.9 -62.1 124.2 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.7734 0.8794
## Residual 1.6135 1.2702
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.61
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 20.647140 7.336939 2.814 0.004891 **
## poly(HD_Cont, 2)1 1.309290 2.601744 0.503 0.614799
## poly(HD_Cont, 2)2 -7.654438 2.200658 -3.478 0.000505 ***
## MHWAfter -4.310017 0.435690 -9.892 < 2e-16 ***
## NPP -0.009987 0.006989 -1.429 0.153020
## poly(HD_Cont, 2)1:MHWAfter -1.134566 2.540487 -0.447 0.655168
## poly(HD_Cont, 2)2:MHWAfter -2.188292 2.540487 -0.861 0.389036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Simpson - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 137.4 151.1 -59.7 119.4 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.1706 0.413
## Residual 1.7981 1.341
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.8
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 16.355969 6.038214 2.709 0.006754 **
## poly(HD_Cont, 2)1 0.333748 2.299570 0.145 0.884604
## poly(HD_Cont, 2)2 -7.333595 1.996033 -3.674 0.000239 ***
## MHWAfter -4.459612 0.459937 -9.696 < 2e-16 ***
## NPP -0.007108 0.005750 -1.236 0.216424
## poly(HD_Cont, 2)1:MHWAfter 0.032907 2.681869 0.012 0.990210
## poly(HD_Cont, 2)2:MHWAfter 0.710243 2.681869 0.265 0.791139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Richness - 95% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 190.6 204.3 -86.3 172.6 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 1.099 1.049
## Residual 8.339 2.888
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 8.34
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 33.23388 13.40053 2.480 0.01314 *
## poly(HD_Cont, 2)1 0.09279 5.05260 0.018 0.98535
## poly(HD_Cont, 2)2 -8.14172 4.37108 -1.863 0.06251 .
## MHWAfter -3.72362 0.99046 -3.759 0.00017 ***
## NPP -0.01735 0.01276 -1.359 0.17405
## poly(HD_Cont, 2)1:MHWAfter 0.98449 5.77534 0.170 0.86464
## poly(HD_Cont, 2)2:MHWAfter -15.78242 5.77534 -2.733 0.00628 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Shannon - 95% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 146.3 160.0 -64.1 128.3 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.7406 0.8606
## Residual 1.9126 1.3830
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.91
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 22.415702 7.603577 2.948 0.003198 **
## poly(HD_Cont, 2)1 1.149400 2.729324 0.421 0.673660
## poly(HD_Cont, 2)2 -8.224279 2.319628 -3.546 0.000392 ***
## MHWAfter -4.722210 0.474357 -9.955 < 2e-16 ***
## NPP -0.010712 0.007243 -1.479 0.139113
## poly(HD_Cont, 2)1:MHWAfter -0.619336 2.765955 -0.224 0.822824
## poly(HD_Cont, 2)2:MHWAfter -3.092995 2.765955 -1.118 0.263466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Simpson - 95% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + NPP + (1 | Site)
## Data: estimates_95 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 142.1 155.9 -62.1 124.1 25
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.07091 0.2663
## Residual 2.18515 1.4782
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.19
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 16.845214 6.298051 2.675 0.00748 **
## poly(HD_Cont, 2)1 0.281470 2.445658 0.115 0.90837
## poly(HD_Cont, 2)2 -7.867812 2.136113 -3.683 0.00023 ***
## MHWAfter -4.788453 0.507027 -9.444 < 2e-16 ***
## NPP -0.007045 0.005997 -1.175 0.24012
## poly(HD_Cont, 2)1:MHWAfter 0.163696 2.956453 0.055 0.95584
## poly(HD_Cont, 2)2:MHWAfter 0.863167 2.956453 0.292 0.77032
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
NPP Sensitivity for 90% Coverage Models
Hill-Richness - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 0)
##
## AIC BIC logLik deviance df.resid
## 160.2 172.4 -72.1 144.2 26
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 2.588 1.609
## Residual 2.236 1.495
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 2.24
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 12.3219 0.5327 23.132 < 2e-16 ***
## poly(HD_Cont, 2)1 -0.4501 3.1060 -0.145 0.88479
## poly(HD_Cont, 2)2 -8.0667 3.1060 -2.597 0.00940 **
## MHWAfter -3.4002 0.5129 -6.630 3.36e-11 ***
## poly(HD_Cont, 2)1:MHWAfter -3.3826 2.9905 -1.131 0.25800
## poly(HD_Cont, 2)2:MHWAfter -9.2746 2.9905 -3.101 0.00193 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Shannon - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 1)
##
## AIC BIC logLik deviance df.resid
## 142.1 154.3 -63.1 126.1 26
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.9632 0.9814
## Residual 1.6135 1.2702
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.61
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 10.1768 0.3893 26.140 < 2e-16 ***
## poly(HD_Cont, 2)1 -0.7091 2.2701 -0.312 0.754748
## poly(HD_Cont, 2)2 -8.0300 2.2701 -3.537 0.000404 ***
## MHWAfter -4.3100 0.4357 -9.892 < 2e-16 ***
## poly(HD_Cont, 2)1:MHWAfter -1.1346 2.5405 -0.447 0.655171
## poly(HD_Cont, 2)2:MHWAfter -2.1883 2.5405 -0.861 0.389039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hill-Simpson - 90% Coverage
## Family: gaussian ( identity )
## Formula: qD ~ poly(HD_Cont, 2) * MHW + (1 | Site)
## Data: estimates_90 %>% filter(Order.q == 2)
##
## AIC BIC logLik deviance df.resid
## 136.9 149.1 -60.4 120.9 26
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Site (Intercept) 0.2667 0.5165
## Residual 1.7981 1.3409
## Number of obs: 34, groups: Site, 17
##
## Dispersion estimate for gaussian family (sigma^2): 1.8
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.90404 0.34851 25.549 < 2e-16 ***
## poly(HD_Cont, 2)1 -1.10294 2.03216 -0.543 0.587307
## poly(HD_Cont, 2)2 -7.60083 2.03216 -3.740 0.000184 ***
## MHWAfter -4.45961 0.45994 -9.696 < 2e-16 ***
## poly(HD_Cont, 2)1:MHWAfter 0.03313 2.68187 0.012 0.990145
## poly(HD_Cont, 2)2:MHWAfter 0.71020 2.68187 0.265 0.791151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1